Shou Yuntao, Lan Haozhi, Cao Xiangyong
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China.
Neural Netw. 2025 Apr;184:107094. doi: 10.1016/j.neunet.2024.107094. Epub 2025 Jan 9.
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations. Graph contrastive learning (GCL) has been shown to be effective in solving the above problems for node classification tasks. Most existing GCL methods are implemented by randomly removing edges and nodes to create multiple contrasting views, and then maximizing the mutual information (MI) between these contrasting views to improve the node feature representation. However, maximizing the mutual information between multiple contrasting views may lead the model to learn some redundant information irrelevant to the node classification task. To tackle this issue, we propose an effective Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck (CGRL) for node classification, which can adaptively learn to mask the nodes and edges in the graph to obtain the optimal graph structure representation. Furthermore, we innovatively introduce the information bottleneck theory into GCLs to remove redundant information in multiple contrasting views while retaining as much information as possible about node classification. Moreover, we add noise perturbations to the original views and reconstruct the augmented views by constructing adversarial views to improve the robustness of node feature representation. We also verified through theoretical analysis the effectiveness of this cross-attempt reconstruction mechanism and information bottleneck theory in capturing graph structure information and improving model generalization performance. Extensive experiments on real-world public datasets demonstrate that our method significantly outperforms existing state-of-the-art algorithms.
图神经网络（GNNs）因其强大的信息聚合能力而受到广泛的研究关注。尽管GNNs取得了成功，但它们中的大多数都存在由少数流行类别导致的图中的流行度偏差问题。此外，真实的图数据集总是包含不正确的节点标签，这阻碍了GNNs学习有效的节点表示。图对比学习（GCL）已被证明在解决节点分类任务的上述问题方面是有效的。大多数现有的GCL方法是通过随机删除边和节点来创建多个对比视图，然后最大化这些对比视图之间的互信息（MI）以改进节点特征表示。然而，最大化多个对比视图之间的互信息可能会导致模型学习到一些与节点分类任务无关的冗余信息。为了解决这个问题，我们提出了一种用于节点分类的有效方法——具有对抗性跨视图重建和信息瓶颈的对比图表示学习（CGRL），它可以自适应地学习掩盖图中的节点和边以获得最优的图结构表示。此外，我们创新性地将信息瓶颈理论引入到GCL中，以在多个对比视图中去除冗余信息，同时保留尽可能多的关于节点分类的信息。而且，我们对原始视图添加噪声扰动，并通过构建对抗视图来重建增强视图，以提高节点特征表示的鲁棒性。我们还通过理论分析验证了这种交叉尝试重建机制和信息瓶颈理论在捕获图结构信息和提高模型泛化性能方面的有效性。在真实世界公共数据集上的大量实验表明，我们的方法显著优于现有的最先进算法。
